Apparatus, system, and method for fraud detection using multiple scan technologies

ABSTRACT

An apparatus, system, and method are disclosed for detecting fraud. A first optical character recognition module optically recognizes a character imprinted with a magnetic ink on a medium using a first optical character recognition algorithm. A second optical character recognition module also optically recognizes the character using a second optical character recognition algorithm. In addition, a magnetic ink character recognition module magnetically recognizes the character using a magnetic recognition algorithm. A voting module determines if the character is potentially fraudulent based on the recognition results of the first optical character recognition module, the second optical character recognition module, and the magnetic ink character recognition module. If the character is potentially fraudulent, a results module communicates a fraud indicator.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to detecting fraud and more particularly relatesto detecting fraud using multiple scan technologies.

2. Description of the Related Art

Retail point of sale (“POS”) devices are often configured to read thecharacters imprinted on a medium such as a paper check using magneticink. The characters typically include bank information such as bankrouting numbers and the number of the bank account that the check isdrawn upon. The POS device may recognize the characters and communicatethe recognized bank information to another POS system such as a cashregister or to an enterprise computer system.

The POS system may recognize characters using recognition modules. Forexample, the POS system may use a magnetic ink character recognition(“MICR”) module to magnetically recognize a character. The MICR modulemagnetically may include a magnetic scanner, scanning the magnetic inkof the character and decoding the scan to recognize the character. ManyPOS devices also employ an optical character recognition (“OCR”) moduleto optically recognize the character. The OCR module may include anoptical scanner that optically scans the character. In addition, the OCRmodule decodes the scan to recognize the character.

The decoded character data comprises recognition results. Therecognition results of the MICR module and the OCR module typicallycomprise a plurality of character candidates, each including aconfidence level. For example, recognition results may comprise thecharacter candidate ‘2’ with a confidence level of ninety-five percent(95%), the character candidate ‘3’ with a confidence level of sixty-fivepercent (65%), and the character candidate ‘8’ with a confidence levelof forty-three percent (43%). The POS device selects the ‘2’ charactercandidate from the recognition results based on the confidence level.

The recognition results of the OCR module may be used to clarify therecognition results of the MICR module. In one embodiment, if a MICRrecognition result for a character is indeterminate, the OCR module'srecognition result may be used to clarify the MICR recognition results.For example, if the MICR module recognition results were a ‘8’ charactercandidate with an eight-four percent (84%) confidence level and a ‘9’character candidate with a confidence level of eight-two percent (82%),the recognition results may be considered indeterminate. The OCRmodule's recognition result of the character candidate ‘9’ with aninety-three percent (93%) confidence level and the character candidate‘8’ with a forty-eight percent (48%) confidence level may be used toresolve the indeterminate recognition result from MICR module and theselect the ‘9’ character candidate.

The MICR module and the OCR module may also have conflicting recognitionresults. Conflicting recognition results may be caused by fraud.Characters may be fraudulently imprinted on a check in order to mimic avalid financial instrument. For example, characters mimicking bankinformation may be printed on a blank check. A forger may create aprinted check that includes one or more characters in the bankinformation that cannot be recognized by the MICR module, the OCRmodule, or both modules.

Unfortunately, the POS device cannot always detect fraud or resolverecognition conflicts with only the recognition results of the MICRmodule and the OCR module. For example, fraudulent bank information on acheck may include anomalies that cause the recognition results of theMICR module and the OCR module to disagree. The POS device may be unableto resolve conflicting recognition results or detect fraud from therecognition result conflict using only the MICR and OCR modules becausethe POS device is limited to two sets of recognition results.

From the foregoing discussion, it should be apparent that a need existsfor an apparatus, system, and method that detects fraud using at leastthree recognition modules. Beneficially, such an apparatus, system, andmethod would increase the ability of a POS device to detect fraudulentinformation.

SUMMARY OF THE INVENTION

The present invention has been developed in response to the presentstate of the art, and in particular, in response to the problems andneeds in the art that have not yet been fully solved by currentlyavailable fraud detection methods. Accordingly, the present inventionhas been developed to provide an apparatus, system, and method for frauddetection that overcome many or all of the above-discussed shortcomingsin the art.

The apparatus to detect fraud is provided with a logic unit containing aplurality of modules configured to functionally execute the necessarysteps of detecting fraudulent printed characters at a retail POS device.These modules in the described embodiments include a MICR module, afirst OCR module, a second OCR module, a voting module, and a resultsmodule.

The MICR module magnetically recognizes a character imprinted on amedium using magnetic ink. The medium may be a check and bankinformation may comprise the character. The MICR module generatesrecognition results from recognizing the character. In one embodiment,the recognition results comprise a plurality of character candidateseach with a confidence level. The first OCR module optically recognizesthe character using a first OCR algorithm and also generates recognitionresults. In addition, the second OCR module optically recognizes thecharacter using a second OCR algorithm, generating recognition results.

The voting module determines if the character is potentially fraudulentbased on the recognition results of the MICR module, the first OCRmodule, and the second OCR module. In one embodiment, the voting moduleuses a look-up table to determine if the character is potentiallyfraudulent. The results module communicates a fraud indicator if thecharacter is potentially fraudulent. In one embodiment, the resultsmodule communicates the fraud indicator to the display of a POS device.

A system of the present invention is also presented to detect fraud. Thesystem may be embodied in a POS receipt printer/scanner. In particular,the system, in one embodiment, includes a memory module, a processormodule, and a detection module comprising a MICR module, a first OCRmodule, a second OCR module, a voting module, and a results module. Inone embodiment, the detection module also includes a transport module.

The MICR module may include a magnetic scanner. In addition, the MICRmodule magnetically recognizes a character imprinted on a check withmagnetic ink using a magnetic recognition algorithm. The first OCRmodule may also include an optical scanner. In one embodiment, theoptical scanner optically scans the check and records the scan. Thefirst OCR module optically recognizes the character from the scan usinga first OCR algorithm. In addition, the second OCR module opticallyrecognizes the character from the scan using a second OCR algorithm.

The voting module determines if the character is potentially fraudulentbased on the recognition results of the MICR module, the first OCRmodule, and the second OCR module. In a certain embodiment, the votingmodule stores the potentially fraudulent character determination to thememory module. The results module communicates a fraud indicator if thecharacter is potentially fraudulent. In one embodiment, the resultsmodule communicates the fraud indicator such as a warning message to thedisplay of a display

In one embodiment, the transport module transports the check relative tothe magnetic scanner and the optical scanner, enabling the magnetic andoptical scans. In addition, the voting module may direct the transportmodule to re-transport the check relative to the magnetic scanner andthe optical scanner so that the check may be re-scanned in response tothe recognition results of the MICR module, the first OCR module, andthe second OCR module.

A method of the present invention is also presented for detecting fraud.The method in the disclosed embodiments substantially includes the stepsnecessary to carry out the functions presented above with respect to theoperation of the described apparatus and system. In one embodiment, themethod includes optically recognizing a character using a first OCRalgorithm, optically recognizing the character using a second OCRalgorithm, magnetically recognizing the character, determining if thecharacter is potentially fraudulent, and communicating a fraudindicator.

A first OCR module optically recognizes a character using a first OCRalgorithm and a second OCR module optically recognizes the characterusing a second OCR algorithm. In addition, a MICR module magneticallyrecognizes the character using a magnetic recognition algorithm. Avoting module determines if the character is potentially fraudulent. Inone embodiment, the voting module uses a decision tree to determine ifthe character is potentially fraudulent. If the character is potentiallyfraudulent, a results module may communicate a fraud indicator.

Reference throughout this specification to features, advantages, orsimilar language does not imply that all of the features and advantagesthat may be realized with the present invention should be or are in anysingle embodiment of the invention. Rather, language referring to thefeatures and advantages is understood to mean that a specific feature,advantage, or characteristic described in connection with an embodimentis included in at least one embodiment of the present invention. Thus,discussion of the features and advantages, and similar language,throughout this specification may, but do not necessarily, refer to thesame embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention can be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

The present invention detects a potentially fraudulent character fromthe recognition results of a MICR module, a first OCR module employing afirst OCR algorithm, and a second OCR employing a second OCR algorithm.In addition, the present invention communicates a fraud indicator if thecharacter is potentially fraudulent.

As is well known by those skilled in the art, MICR technology is basedupon the magnetic characteristics of a character. The OCR technology isbased upon the optical characteristics of a character. If the twotechnologies present different decoding results this may be a result ofpoor magnetic or optical characteristics or may be an indicator of afraudulent check. When the two OCR recognition engines, which areusually in agreement, present different results from that of the MICRengine or if the two OCR results differ this can be used as an indicatorof a potentially fraudulent check and for an operator to more criticallyview the customer's identification.

It is understood that there is no total solution for capturing oridentifying all fraudulent checks. However, as technology continues toview the characteristics of the MICR encoded characters in more detail(such as described via this invention) the level of detection will beenhanced which will result in lower amount of fraudulent checks that areprocessed. These features and advantages of the present invention willbecome more fully apparent from the following description and appendedclaims, or may be learned by the practice of the invention as set forthhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict only typical embodiments of the invention and are nottherefore to be considered to be limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings, in which:

FIG. 1 is a schematic block diagram illustrating one embodiment of a POSsystem in accordance with the present invention;

FIG. 2 is a schematic block diagram illustrating one embodiment of afraud detection apparatus of the present invention;

FIG. 3 is a perspective drawing illustrating one embodiment of a POSsystem of the present invention;

FIG. 4 is a schematic flow chart diagram illustrating one embodiment ofa fraud detection method in accordance with the present invention;

FIG. 5 is a schematic block diagram illustrating one embodiment of alook-up table of the present invention;

FIG. 6 is a schematic flow chart diagram illustrating one embodiment ofa decision tree method of the present invention; and

FIG. 7 is a schematic block diagram illustrating one embodiment of afraud detection host of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Many of the functional units described in this specification have beenlabeled as modules, in order to more particularly emphasize theirimplementation independence. For example, a module may be implemented asa hardware circuit comprising custom very large scale integration(“VLSI”) circuits or gate arrays, off-the-shelf semiconductors such aslogic chips, transistors, or other discrete components. A module mayalso be implemented in programmable hardware devices such as fieldprogrammable gate arrays, programmable array logic, programmable logicdevices or the like.

Modules may also be implemented in software for execution by varioustypes of processors. An identified module of executable code may, forinstance, comprise one or more physical or logical blocks of computerinstructions, which may, for instance, be organized as an object,procedure, or function. Nevertheless, the executables of an identifiedmodule need not be physically located together, but may comprisedisparate instructions stored in different locations which, when joinedlogically together, comprise the module and achieve the stated purposefor the module.

Indeed, a module of executable code may be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different programs, and across several memory devices.Similarly, operational data may be identified and illustrated hereinwithin modules, and may be embodied in any suitable form and organizedwithin any suitable type of data structure. The operational data may becollected as a single data set, or may be distributed over differentlocations including over different storage devices, and may exist, atleast partially, merely as electronic signals on a system or network.

Reference throughout this specification to “one embodiment,” “anembodiment,” or similar language means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present invention. Thus,appearances of the phrases “in one embodiment,” “in an embodiment,” andsimilar language throughout this specification may, but do notnecessarily, all refer to the same embodiment.

Furthermore, the described features, structures, or characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. In the following description, numerous specific details areprovided, such as examples of programming, software modules, userselections, network transactions, database queries, database structures,hardware modules, hardware circuits, hardware chips, etc., to provide athorough understanding of embodiments of the invention. One skilled inthe relevant art will recognize, however, that the invention can bepracticed without one or more of the specific details, or with othermethods, components, materials, and so forth. In other instances,well-known structures, materials, or operations are not shown ordescribed in detail to avoid obscuring aspects of the invention.

FIG. 1 is a schematic block diagram illustrating one embodiment of a POSsystem 100 of the present invention. The system 100 includes a processormodule 105, a memory module 110, a bridge module 115, a communicationmodule 155, a user interface module 160, a printer module 165, a displaymodule 170, and a detection module 175 comprising a MICR module 120, afirst OCR module 125, a second OCR module 130, a voting module 135, aresults module 140, and a transport module 145.

The memory module 110 stores digital instructions and data. In oneembodiment, the memory module 110 comprises a volatile memory such as arandom access memory (“RAM”). Alternatively, the memory module maycomprise a non-volatile memory such as a flash RAM. The memory module110 may also include a programmable read only memory (“PROM”) containingsoftware instructions. The processor module 105 executes the softwareinstructions and manipulates the data as is well known to those ofordinary skill in the art.

The processor module 105 and the memory module 110 communicate throughthe bridge module 115 with the detection module 175, the communicationmodule 155, the user interface module 160, the printer module 165, andthe display module 170. The communication module 155 communicates withone or more external devices. In one embodiment, the communicationmodule 155 communicates with another POS system such as a cash register.In an alternate embodiment, the communication module 155 communicatesover a network to an enterprise computer system.

The user interface module 160 receives user data input and commands. Inone embodiment, the user interface module 160 includes one or more inputkeys. In an alternate embodiment, the user interface module 160 isconfigured as a touch screen. In one embodiment, the printer module 165prints a customer receipt. In addition, the printer module 165 may printupon a medium such as a check. The display module 170 conveysinformation to the user. In one embodiment, the display module 170 isconfigured as one or more light emitting diodes (“LED”) each conveying aspecified message. In an alternate embodiment, the display module 170displays a plurality of pixels in for example a liquid crystal display(“LCD”).

The system 100 processes the medium. In one embodiment, the medium is acheck and includes one or more characters imprinted with a magnetic ink.Each character is magnetically and optically recognizable. Thecharacters may comprise bank information such as a bank routing numberand a bank account number. The system 100 recognizes the charactersusing the MICR module 120, the first OCR module 125, and the second OCRmodule 130 recognition modules 120, 125, 130 with each recognitionmodule 120, 125, 130 contributing recognition results. The system 100may employ the recognition results from the at least three recognitionmodules 120, 125, 130 to process the medium as part of a transaction. Inaddition, the system 100 may employ the recognition results to detectfraud.

The first OCR module 125 may include an optical scanner 185. The opticalscanner 185 optically scans a character. In addition, the first OCRmodule 125 may comprise one or more software programs executing on theprocessor module 105. The first OCR module 125 recognizes the characterby scanning the character and decoding the character using a first OCRalgorithm. In one embodiment, the first OCR module 125 generatesrecognition results. The recognition results may comprise a plurality ofcharacter candidates. For example, the first OCR module 125 may decode ascan of a character and generate the recognition result of threecharacter candidates such as ‘2,’ ‘3,’ and ‘8.’ In addition, therecognition result may include a confidence level such as eighty percent(80%) for each character candidate. The confidence level may be aprobability that the character candidate corresponds to the character.

The second OCR module 130 recognizes the character using a second OCRalgorithm from the scan of the optical scanner 185. In addition, thesecond OCR module 130 also generates recognition results. The second OCRmodule 130 may also comprise one or more software programs executing onthe processor module 105.

The MICR module 120 may include a magnetic scanner 180. In addition, theMICR module 120 may comprise one or more software programs that executeon the processor module 105. The magnetic scanner 180 magnetically scansthe character. The MICR module 120 recognizes the character by employinga magnetic recognition algorithm to decode magnetic scan. In addition,the MICR module 120 generates recognition results from the applicationof the magnetic recognition algorithm. Although the system 100 isdepicted with one MICR module 120 and two OCR modules 125, 130, anynumber of MICR modules 120 and two or more OCR modules 125, 130 may beemployed.

The voting module 135 determines if the character is potentiallyfraudulent based on the recognition results of the MICR module 120, thefirst OCR module 125, and the second OCR module 130. In one embodiment,the voting module 135 is configured as a software program executing onthe processor module 105. The results module 140 communicates a fraudindicator if the voting module 135 determines the character ispotentially fraudulent. For example, the results module 140 maycommunicate a warning message to the display module 170. The system 100detects potential fraud using at least three recognition modules 120,125, 130.

In one embodiment, the transport module 145 transports the mediumrelative to the magnetic scanner 180 and the optical scanner 185,enabling the magnetic and optical scans of the character. In a certainembodiment, the transport module 145 comprises one or more motorizedrollers. The transport module 145 may re-transport the medium relativeto the magnetic scanner 180 and the optical scanner 185 based on therecognition results of the MICR module 120, the first OCR module 125,and the second OCR module 130. For example, if the recognition resultsof the first OCR module 125 and the second OCR module 130 generateconflicting recognition results for a character, the transport module145 may re-transport the medium and the optical detection module 185 mayre-scan the medium. In addition, the first OCR module 125 and the secondOCR module 130 may again decode the character and generate recognitionresults.

FIG. 2 is a schematic block diagram illustrating one embodiment of afraud detection apparatus 200 of the present invention. The first OCRmodule 125 optically recognizes a character using a first OCR algorithm.The character is printed with a magnetic ink and may be recognizedoptically and magnetically. For example, the character may comprise amagnetic character from a font such as E-13B or CMC-7. Alternatively,the character may comprise an OCR font such as OCR-A and OCR-B.

In one embodiment, the first OCR module 125 comprises an optical scanner185 and scans the character. In an alternate embodiment, the first OCRmodule 125 receives the scan of the character. The first OCR module 125further decodes the scan using a first OCR algorithm and generatesrecognition results. In addition, the second OCR module 130 alsooptically recognizes the character using a second OCR algorithm,generating additional recognition results. In one embodiment, the secondOCR module 130 decodes the scan produced by the optical scanner of thefirst OCR module 125.

The MICR module 120 magnetically recognizes the character using amagnetic recognition algorithm and generates recognition results. Inaddition, the MICR module 120 may detect the magnetic signal strength ofthe magnetic ink of the character. The MICR module 120 may detect nomagnetic signal, a low-strength magnetic signal, or an expected strengthmagnetic signal. The low-strength magnetic signal as used herein refersto a magnetic signal strength fifty percent (50%) less than a specifiedmagnetic signal strength for characters imprinted with magnetic ink. Thespecified magnetic signal strength may apply to financial instrumentssuch as checks and may vary from country to country. The expectedstrength magnetic signal as used herein refers to a magnetic signalstrength greater than fifty percent (50%) of the specified magneticsignal strength.

The voting module 135 determines if the character is potentiallyfraudulent based on the recognition results of the MICR module 120, thefirst OCR module 125, and the second OCR module 130. In addition, thevoting module 135 may base the determination in part on the magneticsignal strength of the character. For example, the voting module 135 maydetermine that a character is potentially fraudulent if the characterhas no detectable magnetic signal.

In one embodiment, the voting module 135 uses a look-up table todetermine if the character is potentially fraudulent. In a certainembodiment, the voting module 135 may define a plurality ofrelationships based on the recognition results. The relationships mayinclude but are not limited to: the MICR module's 120 charactercandidate with the highest confidence level being equivalent to thefirst OCR module's 125 character candidate with the highest confidencelevel; the first OCR module's 125 character candidate with the highestconfidence level being equivalent to the second OCR module's 130character candidate with the highest confidence level; and the MICRmodule's 120 character candidate with the highest confidence level beingequivalent to the second OCR module's 130 character candidate with thehighest confidence level.

The voting module 135 may use the plurality of relationships asconditions that address the look-up table and select a result. Forexample, if the character candidate with the highest confidence level ofthe MICR module 120, the first OCR module 125, and the second OCR module130 are the same, and if the MICR module 120 detected the expectedmagnetic signal strength at the character, the voting module 135 maydetermine that the character is not potentially fraudulent.Alternatively, if the character candidate with the highest confidencelevel of the MICR module 120 differs from the character candidate withthe highest confidence level of the first OCR module 125 and the secondOCR module 130, the voting module 135 may determine that the characteris potentially fraudulent.

The results module 140 communicates a fraud indicator if the votingmodule 135 determines the character is potentially fraudulent. In oneembodiment, the results module 140 communicates the fraud indicator tothe display module 170 of a POS system 100 such as a printer, cashregister, and the like. For example, the results module 140 mayilluminate an LED to indicate that a check is potentially fraudulent andto prompt an operator to refuse to accept the check, ask for additionalidentification, or the like.

Using the recognition results of at least three recognition modules suchas the MICR module 120, the first OCR module 125, and the second OCRmodule 130 allows the apparatus 200 to determine if a character ispotentially fraudulent with increased accuracy by expanding theavailable information when two recognition modules disagree. Forexample, if the recognition results of the MICR module 120 and the firstOCR module 125 are not equivalent, the voting module 135 may use therecognition results of the second OCR module 130 to improve therecognition of the character and determine if the character ispotentially fraudulent.

FIG. 3 is a perspective drawing illustrating one embodiment of a POSsystem 300 of the present invention. The system 300 includes a paperdrum 305, an exit slot 310, a user input 315, and an LED 320. In oneembodiment, the system 300 is a TI 8 printer manufactured byInternational Business Machines Corporation of Armonk, N.Y.

The paper drum 305 contains a replaceable roll of paper or otherprintable medium. The system 300 imprints characters on the paper usingthermal printing technology, impact printing technology, or the like.The printed paper emerges from the exit slot 310.

The user input 315 comprises a push button and is configured to receiveuser input. In one embodiment, the user input 315 is configured as theuser interface module 160 of FIG. 1. The LED 320 may indicate one ormore status states of the system 300. In one embodiment, the LED 320 isthe display module 170 of FIG. 1. In a certain embodiment, the LED 320displays a fraud indicator.

The following schematic flow chart diagrams that follow are generallyset forth as logical flow chart diagrams. As such, the depicted orderand labeled steps are indicative of one embodiment of the presentedmethod. Other steps and methods may be conceived that are equivalent infunction, logic, or effect to one or more steps, or portions thereof, ofthe illustrated method. Additionally, the format and symbols employedare provided to explain the logical steps of the method and areunderstood not to limit the scope of the method. Although various arrowtypes and line types may be employed in the flow chart diagrams, theyare understood not to limit the scope of the corresponding method.Indeed, some arrows or other connectors may be used to indicate only thelogical flow of the method. For instance, an arrow may indicate awaiting or monitoring period of unspecified duration between enumeratedsteps of the depicted method. Additionally, the order in which aparticular method occurs may or may not strictly adhere to the order ofthe corresponding steps shown.

FIG. 4 is a schematic flow chart diagram illustrating one embodiment ofa fraud detection method 400 of the present invention. A first OCRmodule 125 optically recognizes 405 a character imprinted on a mediumsuch as a check using a first OCR algorithm. In one embodiment, thefirst OCR algorithm is a line segmentation OCR algorithm as is wellknown to those skilled in the art. In an alternate embodiment, the firstOCR algorithm is a hidden Markov model OCR algorithm. Recognizing 405the character generates recognition results.

A second OCR module 130 optically recognizes 410 the character using asecond OCR algorithm and generating recognition results. In oneembodiment, the second OCR algorithm is a statistical patternrecognition OCR algorithm. The second OCR algorithm may also be a linesegmentation or a hidden Markov model OCR algorithm, but must differfrom the first OCR algorithm.

A MICR module 120 magnetically recognizes 415 the character using amagnetic recognition algorithm and generates recognition results. Thecharacter is imprinted using a magnetic ink. In one embodiment, thecharacter is organized on a seven by eleven (7×11) square grid. In analternate embodiment, the character is organized as seven vertical barsseparated by spaces of varying size.

A voting module 135 determines 420 if the character is potentiallyfraudulent. In one embodiment, the voting module 135 uses a decisiontree to determine 420 if the character is potentially fraudulent. Inaddition, the voting module 135 may employ information on the strengthof the magnetic signal from the MICR module 120 to determine 420 if thecharacter is potentially fraudulent. Although in the depictedembodiment, the voting module 135 determines if one character ispotentially fraudulent, the voting module 135 may determine if aplurality of characters are potentially fraudulent.

If the voting module 135 determines 420 the character is potentiallyfraudulent, a results module 140 may communicate 425 a fraud indicator.For example, the results module 140 may communicate 425 a fraudindicator instruction via a LCD display module 170 directing an operatorto ask for photographic identification or to get approval from amanager. In one embodiment, the results module 140 stores 435 thepotentially fraudulent character determination. For example, the resultsmodule 140 may append the potentially fraudulent character determinationto a transaction that is stored.

In one embodiment, if the voting module 135 determines 420 the characteris not potentially fraudulent, the results module 140 communicates 430an acceptance indicator. In one embodiment, the acceptance indicatorcommunicates 430 that no additional approval or documentation isrequired to accept a check. For example, the results module 140 mayilluminate an acceptance LED display module 170 indicating that thecheck is accepted. Alternatively, the results module 140 may communicate430 an acceptance message via a LCD display module 170 to the operator.The method 400 detects potentially fraudulent characters using therecognition results from at least three distinct algorithms.

FIG. 5 is a schematic block diagram illustrating one embodiment of alook-up table 500 of the present invention. The look-up table comprisesa plurality of conditions including a no magnetic signal condition 505,a low strength magnetic signal condition 510, a MICR result equals firstOCR result condition 515, a MICR result equals second OCR resultcondition 520, and a first OCR result equals second OCR result condition525. The table 500 further comprises a plurality of table entries 535,each with a unique set of condition values 505, 510, 515, 520, 525 and acommunication value 530.

A voting module 135 may determine if a character is potentiallyfraudulent by comparing a set of observed conditions values to thecondition values 505, 510, 515, 520, 525 of the look-up table 500 andselecting a table entry 535 with corresponding conditions. For example,if the MICR result equals second OCR result condition 520 is asserted orequal to one (1) and all other conditions 505, 510, 515, 525 are notasserted or equal to zero (0), the voting module 135 may select thecorresponding table entry 535 d. In addition, results module 140 maycommunicate the communication value 530 of corresponding table entry 535d as the fraud indicator. The look-up table 500 may be used to determine420 if a character is potentially fraudulent and provide an appropriatefraud indicator or acceptance indicator.

FIG. 6 is a schematic flow chart diagram illustrating one embodiment ofa decision tree method 600 of the present invention. A voting module 135determines 605 if a character has a magnetic signal. If the characterhas no magnetic signal, the results module 140 may communicate 630 asignificant fraud indicator and the decision tree method 600 terminates.The significant fraud indicator may indicate that the character islikely to be fraudulent and direct, for example, that an operator shouldnot accept a medium such as a check imprinted with the character.

If the character has a magnetic signal, the voting module 135 mayfurther determine 610 if the character has a low magnetic signalstrength. In one embodiment, the voting module 135 determines 610 has alow magnetic signal strength by comparing the character's signalstrength to a specified value. If the character has a low strengthmagnetic signal, the results module 140 communicates 645 a fraudindicator and the method 600 terminates.

If the character does not have a low strength magnetic signal, thevoting module 135 determines 615 if the MICR module 120 recognitionresults are equivalent to the first OCR module 125 recognition results.For example, the voting module 135 may compare the highest confidencelevel character candidate of the MICR module 120 to the highestconfidence level character candidate of the first OCR module 125 anddetermine 615 the recognition results are equivalent if both the highestconfidence level character candidates are equivalent.

If the MICR module 120 recognition results are not equivalent to thefirst OCR module 125 recognition results, the voting module 135determines 640 if the first OCR module 125 recognition results areequivalent to the second OCR module 130 recognition results. If thefirst OCR module 125 recognition results are equivalent to the secondOCR module 130 recognition results, the results module 140 communicates625 an acceptance indicator and the method 600 terminates. If the firstOCR module 125 recognition results are not equivalent to the second OCRmodule 130 recognition results, the results module 140 communicates 645a fraud indicator and the method 600 terminates.

If the MICR module 120 recognition results are equivalent to the firstOCR module 125 recognition results, the voting module 135 determines 620if the first OCR module 125 recognition results are equivalent to thesecond OCR module 130 recognition results. If the first OCR module 125recognition results are not equivalent to the second OCR module 130recognition results, the transport module 145 re-transports 635 themedium of the character allowing a optical scanner 185 to re-scan thecharacter. In addition, the voting module 135 again determines 640 ifthe first OCR module 125 recognition results are equivalent to thesecond OCR module 130 recognition results. If the first OCR module 125recognition results are equivalent to the second OCR module 130recognition results, the results module 140 communicates 625 anacceptance indicator and the decision tree method 600 terminates. If thefirst OCR module 125 recognition results are not equivalent to thesecond OCR module 130 recognition results, the results module 140communicates 645 a fraud indicator and the method 600 terminates.

If the voting module 135 determines 620 that the first OCR module 125recognition results are equivalent to the second OCR module 130recognition results, the results module 140 communicates 625 anacceptance indicator and the decision tree method 600 terminates. Themethod 600 detects a potentially fraudulent character using conditionsderived from three or more recognition modules 120, 125, 130.

FIG. 7 is a schematic block diagram illustrating one embodiment of afraud detection host 700 of the present invention. The host 700 may bean enterprise computer system in communication with a POS system 100. Inthe depicted embodiment, the host 700 includes a MICR module 120, afirst OCR module 125, a second OCR module 130, a voting module 135, aresults module 140, and a communications interface module 705.

The communications interface module 705 receives a magnetic scan of acharacter from a magnetic scanner 180 and an optical scan of thecharacter from an optical scanner 185. In one embodiment, the POS system100 comprises the magnetic scanner 180 and the optical scanner 185. TheMICR module 120 recognizes the character using a magnetic recognitionalgorithm. The first OCR module 125 also recognizes the character usinga first OCR algorithm and the second OCR module 130 recognizes thecharacter using a second OCR algorithm. The voting module 135 determinesif the character is potentially fraudulent based on the recognitionresults of the MICR module 120, the first OCR module 125, and the secondOCR module 130. In addition, the results module 140 communicates a fraudindicator if the character is potentially fraudulent. In one embodiment,the results module 140 communicates the fraud indicator through thecommunications interface module 705 to the POS system 100.

The present invention detects a potentially fraudulent character fromthe recognition results of a MICR module 120 employing a magneticcharacter recognition algorithm, a first OCR module 135 employing afirst OCR algorithm, and a second OCR 130 employing a second OCRalgorithm. In addition, the present invention communicates a fraudindicator if the character is potentially fraudulent. Thus, the presentinvention detects potential fraud using the recognition results of atleast three recognition modules. The present invention may be embodiedin other specific forms without departing from its spirit or essentialcharacteristics. The described embodiments are to be considered in allrespects only as illustrative and not restrictive. The scope of theinvention is, therefore, indicated by the appended claims rather than bythe foregoing description. All changes which come within the meaning andrange of equivalency of the claims are to be embraced within theirscope.

1. An apparatus to detect fraud, the apparatus comprising: a MICR moduleconfigured to recognize a character imprinted on a medium using magneticink; a first OCR module configured to recognize the character using afirst OCR algorithm; a second OCR module configured to recognize thecharacter using a second OCR algorithm; a voting module configured todetermine if the character is potentially fraudulent based on therecognition results of the MICR module, the first OCR module, and thesecond OCR module; and a results module configured to communicate afraud indicator if the character is potentially fraudulent.
 2. Theapparatus of claim 1, wherein the medium is configured as a check. 3.The apparatus of claim 1, wherein the MICR module comprises a magneticscanner and the first OCR module comprises an optical scanner andfurther comprising a transport module configured to transport the mediumrelative to the magnetic scanner and the optical scanner.
 4. Theapparatus of claim 3, wherein the voting module is configured to directthe transport module to re-transport the medium based on the recognitionresults of the MICR module, the first OCR module, and the second OCRmodule.
 5. The apparatus of claim 1, wherein the recognition results ofthe MICR module, the first OCR module, and the second OCR module eachcomprise a plurality of character candidates each with a confidencelevel.
 6. The apparatus of claim 1, wherein the voting module is furtherconfigured to determine if the character is potentially fraudulent usinga look-up table.
 7. The apparatus of claim 1, wherein the voting moduleis further configured to determine if the character is potentiallyfraudulent using a decision tree.
 8. An apparatus to detect fraud, theapparatus comprising: a communication interface module configured toreceive an optical scan and a magnetic scan of a character imprinted ona check using magnetic ink; a MICR module configured to recognize thecharacter from the magnetic scan; a first OCR module configured torecognize the character from the optical scan using a first OCRalgorithm; a second OCR module configured to recognize the characterfrom the optical scan using a second OCR algorithm; a voting moduleconfigured to determine if the character is potentially fraudulent basedon the recognition results of the MICR module, the first OCR module, andthe second OCR module; and a results module configured to communicate afraud indicator if the character is potentially fraudulent.
 9. A systemto detect fraud, the system comprising: a memory module; a processormodule; and a detection module comprising: a MICR module configured torecognize a character imprinted on a check using magnetic ink; a firstOCR module configured to recognize the character using a first OCRalgorithm; a second OCR module configured to recognize the characterusing a second OCR algorithm; a voting module configured to determine ifthe character is potentially fraudulent based on the recognition resultsof the MICR module, the first OCR module, and the second OCR module; anda results module configured to communicate a fraud indicator if thecharacter is potentially fraudulent.
 10. The system of claim 9, whereinthe MICR module further comprises a magnetic scanner, the first OCRmodule comprises an optical scanner, and the detection module comprisesa transport module configured to transport the medium relative to themagnetic scanner and the optical scanner.
 11. The system of claim 10,wherein the voting module is configured to direct the transport moduleto re-transport the check based on the recognition results of the MICRmodule, the first OCR module, and the second OCR module.
 12. The systemof claim 9, further comprising a printer module configured to print apoint of sale record.
 13. The system of claim 9, further comprising acommunication module configured to communicate with an enterprisecomputer system.
 14. The system of claim 9, wherein the recognitionresults of the MICR module, the first OCR module, and the second OCRmodule each comprise a plurality of character candidates each with aconfidence level.
 15. The system of claim 9, wherein the voting moduleis further configured to determine if the character is potentiallyfraudulent based on a look-up table.
 16. The system of claim 9, whereinthe voting module is further configured to store the potentiallyfraudulent character determination to the memory module.
 17. A signalbearing medium tangibly embodying a program of machine-readableinstructions executable by a digital processing apparatus to performoperations to detect fraud, the operations comprising: opticallyrecognizing a character imprinted on a medium with a magnetic ink usinga first OCR algorithm; optically recognizing the character using asecond OCR algorithm; magnetically recognizing the character using amagnetic recognition algorithm; determining if the character ispotentially fraudulent based on the recognition results of the first OCRalgorithm, the second OCR algorithm, and the magnetic recognitionalgorithm; and communicating a fraud indicator.
 18. The signal bearingmedium of claim 17, wherein the instructions further comprise operationsto the transport the medium.
 19. The signal bearing medium of claim 17,wherein the instructions further comprise operations to re-transport themedium in response to the recognition results of the first OCRalgorithm, the second OCR algorithm, and the magnetic recognitionalgorithm.
 20. The signal bearing medium of claim 17, wherein theinstructions further comprise operations to calculate a confidence levelfor each of a plurality of character candidates.
 21. The signal bearingmedium of claim 17, wherein the instructions further comprise operationsto determine if the character is potentially fraudulent using a look-uptable.
 22. The signal bearing medium of claim 17, wherein theinstructions further comprise operations to determine if the characteris potentially fraudulent using a decision tree.
 23. The signal bearingmedium of claim 17, wherein the instructions further comprise operationsto store the potentially fraudulent character determination.
 24. Thesignal bearing medium of claim 17, wherein the first OCR algorithm is aline segmentation OCR algorithm.
 25. The signal bearing medium of claim17, wherein the second OCR algorithm is a statistical patternrecognition OCR algorithm.
 26. The signal bearing medium of claim 17,wherein the first OCR algorithm is a hidden Markov model OCR algorithm.27. A method for detecting fraud, the method comprising: opticallyrecognizing a character imprinted on a medium with a magnetic ink usinga first OCR algorithm; optically recognizing the character using asecond OCR algorithm; magnetically recognizing the character using amagnetic recognition algorithm; determining if the character ispotentially fraudulent based on the recognition results of the first OCRalgorithm, the second OCR algorithm, and the magnetic recognitionalgorithm; and communicating a fraud indicator.
 28. The method of claim27, wherein the method comprises transporting the medium.
 29. The methodof claim 28, further comprising re-transporting the medium based on therecognition results of the first OCR algorithm, the second OCRalgorithm, and the magnetic recognition algorithm.
 30. An apparatus todetect fraud, the apparatus comprising: means for optically recognizinga character imprinted on a medium with a magnetic ink using a first OCRalgorithm; means for optically recognizing the character using a secondOCR algorithm; means for magnetically recognizing the character using amagnetic recognition algorithm; means for determining if the characteris potentially fraudulent based on the recognition results of the firstOCR algorithm, the second OCR algorithm, and the magnetic recognitionalgorithm; and means for communicating a fraud indicator.